TWI732650B - Stock prediction method and server end for stock prediction - Google Patents

Abstract

A server for stock prediction. When the server receives an analysis request related to a stock, the server generates multiple stock technical indicators related to the stock according to the stock prices of multiple stocks related to the stock, and According to the technical indicators of these individual stocks, multiple stock price forecasts corresponding to individual stocks are generated. On the other hand, the server generates multiple stock prices related to the stock fund based on the multiple fund stock prices corresponding to a stock fund related to the overall stock market trend. Multiple fund technical indicators, and generate multiple stock price forecasts corresponding to stock funds based on these fund technical indicators, and generate multiple stock price forecasts based on the stock price forecasts and the stock fund forecasts Corresponding to the revised stock price forecast value of individual stocks.

Description

Stock prediction method and server end for stock prediction

The present invention relates to a data analysis method suitable for business, in particular to a prediction method that generates a stock price prediction value.

In today's society, the price index is rising, and many people earn a fixed salary by going to work, while also increasing their income through other investment methods. In the investment market, stocks have always been regarded as the most important method of investment and financial management. Therefore, finding a stock that can make stable profits has always been the goal pursued by all investment and financial management professionals.

In the current investment market, many investors use stock analysis software to analyze whether a company’s stock can be invested. Although stock analysis software can assist investors in their use needs, there are still problems with analysis. In more detail, the existing The stock analysis software analyzes the company’s stocks based on the company’s business-related data in multiple unit hours. The business-related data includes revenue per unit of time, earnings per share, operating gross profit margin, and shareholder’s equity. Return rate, operating profit rate, etc., while ignoring the influence of the overall stock market trend. For example, when the overall stock market trend is rising, a stock that is not well developed may still be affected by the overall stock market and show an upward trend to attract investment However, this stock is very likely to continue to fall when the overall stock market trend slows down and even be classified as a fully delivered stock, causing serious losses in investors’ assets.

Therefore, the purpose of the present invention is to provide a stock prediction method capable of predicting the stock price based on the overall stock market trend.

Furthermore, the purpose of the present invention is to provide a server that can predict the stock price based on the overall stock market trend.

Therefore, the stock prediction method of the present invention is implemented by a server connected to a management terminal. The server terminal stores multiple stock prices of individual stocks in a current time interval, and a stock fund related to the overall stock market trend. Multiple fund stock prices in the current time interval, one used to generate multiple technical analysis models based on multiple stock prices, and one used to generate multiple technical indicators related to a stock to be predicted in the current time interval A stock analysis model related to the stock price prediction value of the stock price of the stock to be predicted in a future time interval that is later than the current time interval, and one for multiple techniques related to a stock fund to be predicted in the current time interval The indicator generates a number of overall analysis models related to the stock price forecast value of the stock price of the stock fund to be predicted in the future time interval, and an overall analysis model for the stock price forecast value of the stock fund to be forecast and the stock fund to be forecast The stock price forecast values of the stocks to be predicted are generated, and the stock price forecast values of the stocks to be predicted are affected by the stock price forecast values of the stock funds. Step (A), one step (B), one step (C), one step (D), and one step (E).

In this step (A), when the server receives an analysis request from the management terminal related to the stock, the server uses the technical analysis model based on the stock prices related to the stock , To generate multiple technical indicators related to the individual stock.

In this step (B), the server uses the individual stock analysis model to generate multiple stock price forecasts corresponding to the individual stocks based on the individual stock technical indicators.

In this step (C), the server uses the technical analysis model to generate multiple fund technical indicators related to the stock fund based on the fund stock prices related to the stock fund.

In this step (D), the server uses the overall analysis model to generate multiple stock price forecasts corresponding to the stock fund based on the fund technical indicators.

In this step (E), by the server, based on the stock price forecast values of the stock and the stock fund stock price forecast values, the revised analysis model is used to generate multiple revised stock prices corresponding to individual stocks Predictive value.

In addition, the server of the present invention is used for stock forecasting and is connected to a management terminal via a communication network. The server includes a server communication module, a server storage module, and a server processing module.

The server-side communication module is connected to the communication network, and the server-side storage module stores multiple stock prices of individual stocks in a current time interval, and a stock fund related to the overall stock market trend in the current time interval. A number of fund stock prices, a technical analysis model used to generate multiple technical indicators based on multiple stock prices, and a number of technical analysis models used to generate multiple technical indicators related to a stock to be predicted in the current time interval. A stock analysis model that predicts the stock price prediction value of a stock price in a future time interval later than the current time interval, and one is used to generate multiple transactions based on multiple technical indicators related to a stock fund to be predicted in the current time interval The overall analysis model related to the stock price forecast value of the stock fund to be predicted in the future time interval, and a method used to calculate the stock price forecast value of the stock fund to be forecasted and the stock price of the stock fund to be forecasted The forecast value generates a revised analysis model of multiple revised stock price forecast values in which the stock price forecast values of the stocks to be predicted are affected by the stock price forecast values of the stock funds to be predicted.

The server-side processing module is electrically connected to the server-side communication module and the server-side storage module, wherein when the server-side processing module receives the analysis related to the stock from the management side through the server-side communication module Upon request, the server-side processing module uses the technical analysis model to generate multiple stock technical indicators related to the individual stock based on the stock prices related to the individual stock, and uses the individual stock analysis model based on the individual stock technical indicators Generate multiple stock price forecasts corresponding to individual stocks, and use the technical analysis model to generate multiple fund technical indicators related to the stock fund based on the fund stock prices related to the stock fund, and based on the fund technology Indicators, using the overall analysis model to generate multiple stock price forecasts corresponding to the stock fund, and according to the stock price forecasts and the stock fund forecasts, using the modified analysis model to generate multiple stock prices Corresponding to the revised stock price forecast value of individual stocks.

The effect of the present invention is that the server uses the modified analysis model to generate multiple revised stock price forecasts corresponding to individual stocks, thereby generating stock price forecasts based on the overall stock market trend, thereby allowing investors to be more objective Information to invest.

Before the present invention is described in detail, it should be noted that in the following description, similar elements are denoted by the same numbers.

Referring to FIG. 1, a first embodiment of the stock prediction method of the present invention is implemented by a stock prediction system as shown in FIG. 1 to implement a stock analysis model establishment procedure and an overall analysis model establishment included in the first embodiment Procedures, a modification analysis model establishment procedure, and a prediction procedure, whereby the forecast value of stock prices is generated according to the overall stock market trend, so that investors can invest with more objective information, thereby avoiding possible serious asset losses.

The stock prediction system includes a server terminal 1 for stock prediction of the present invention, and a management terminal 2 connected to the server terminal 1 through a communication network 100, and the server terminal 1 includes a server connected to the communication network 100 A server-side communication module 11, a server-side storage module 12 for storing data, and a server-side processing module 13 electrically connected to the server-side communication module 11 and the server-side storage module 12. Here, The server 1 is, for example, a cloud server, a super computer, a personal computer, or any other similar device.

The server-side storage module 12 stores the stock prices of a single stock in a current time interval, and a stock fund (Exchange Traded Funds, ETF) related to the overall stock market trend, such as Dow Jones Industrial Index Fund, Nass Tak 100 Technology Index Fund, or Yuanta Taiwan Excellence 50 Securities Investment Trust Fund (Taiwan 50 for short), multiple fund stock prices in the current time interval, a technology used to generate multiple technical indicators based on multiple stock prices Analytical model, multiple stock training data corresponding to multiple training stocks, multiple fund training data corresponding to multiple training stock funds, and multiple revised training data. Among them, each stock training data includes corresponding training stocks Multiple training stock technical indicators in a previous time interval earlier than the current time interval, and multiple training stock stock prices in the current time interval, each fund training data includes the corresponding training stock fund at the previous time Multiple training fund technical indicators in the interval, and multiple training fund stock prices in the current time interval, each modified training data includes multiple training stock stock prices corresponding to the training stock in the current time interval, corresponding to the training stock The stock prices of multiple training funds of the type fund in the current time interval, and the stock prices of multiple training correction stocks corresponding to the training stocks in the current time interval.

The management terminal 2 is held by a manager, and includes a management terminal communication module 21, a management terminal input module 22, and an electrical connection between the management terminal communication module 21 and the management terminal input module 22 The management terminal processing module 23, wherein the management terminal communication module 21 is connected to the communication network 100, and the management terminal input module 22 is used for the manager to perform input operations. Here, the management terminal 2 is, for example, a personal Any of computers, tablets, notebooks, or other similar devices.

The procedure for establishing a stock analysis model includes a step 31, a step 32, a step 33, a step 34, and a step 35, which are used to establish a method for establishing a method based on the technologies related to a stock to be predicted in the current time interval. Indicators to generate the individual stock analysis models related to the stock price forecast value of the stock to be predicted in a future time interval that is later than the current time interval.

Referring to Figures 1 and 2, when step 31 is performed, the server-side processing module 13 divides the individual stock training data stored in the server-side storage module 12 into a training subset and a test subset; In this step 32, the server-side processing module 13 uses a machine learning algorithm, such as a support vector machine ( Support Vector Machine (SVM) or logistic regression (Logistic regression) to establish a stock price related to the stock to be predicted in the future time interval based on the technical indicators related to the stock to be predicted in the current time interval The first training model of the predicted value; the step 33 is performed after the first training model is established by the server-side processing module 13, and the server-side processing module 13 is used according to the training data corresponding to each strand in the test subset The technical indicators of the training stocks and the stock prices of the training stocks determine whether the prediction accuracy of the first training model is greater than a first threshold. When the server-side processing module 13 determines the prediction of the first training model When the accuracy rate is not greater than the first threshold value, the server-side processing module 13 immediately performs the step 34, adjusts the first training model and executes the step 33 again; when the server-side processing module 13 determines that the When the prediction accuracy of the first training model is greater than the first threshold, the server-side processing module 13 performs the step 35 to confirm that the first training model is the individual stock analysis model.

The overall analysis model establishment procedure includes a step 41, a step 42, a step 43, a step 44, and a step 45, which is used to establish a method according to the amount related to a stock fund to be predicted in the current time interval. The technical indicators generate multiple overall analysis models related to the stock price forecast value of the stock fund to be predicted in the future time interval.

Referring to Figures 1 and 3, when step 41 is performed, the server-side processing module 13 divides the fund training data stored in the server-side storage module 12 into another training subset and another test sub-set Next, in the step 42, the server-side processing module 13 uses the machine learning algorithm according to the training fund technical indicators and the training fund stock prices corresponding to the training data of each fund in the other training subset, For example, Support Vector Machine (SVM) or logistic regression (Logistic regression) is established to generate the stocks related to the stocks to be predicted based on the technical indicators related to the stocks to be predicted in the current time interval. The second training model of the stock price prediction value of the stock price of the fund in the future time interval; after the second training model is established by the server processing module 13, the step 43 is performed, and the server processing module 13 performs the second training model according to the other In a test subset, the training fund technical indicators and the training fund stock prices corresponding to the training data of each fund are determined to determine whether the prediction accuracy of the second training model is greater than a second threshold. When the server-side processing model When the group 13 judges that the prediction accuracy rate of the second training model is not greater than the second threshold, it immediately uses the step 44 to adjust the second training model and returns to the step 43; on the other hand, when the servo When the end processing module 13 determines that the prediction accuracy of the second training model is greater than the second threshold, the step 45 is performed, and the server end processing module 13 confirms that the second training model is the overall analysis model.

The procedure for establishing the modified analysis model includes a step 51, a step 52, a step 53, a step 54, and a step 55, which are used to establish a method for establishing a stock price prediction value based on the stock price to be predicted and the stock price to be predicted. The stock price forecast values of the stock-type fund generate multiple revised analysis models for the stock price forecast values of the stock-to-be-predicted stocks that are affected by the stock price forecast values of the stock-type fund to be forecasted.

Referring to Figures 1 and 4, when step 51 is performed, the server-side processing module 13 divides the modified training data stored in the server-side storage module 12 into another training subset and another test sub-set Then, when step 52 is performed, the server-side processing module 13 calculates the stock prices of the training stocks, the training funds, and the stock prices of the training corrections based on each modified training data in the another training subset, Using machine learning algorithms, such as Graph Neural Network (GNN), create a method based on the stock price forecast values corresponding to the stocks to be predicted and the stock price forecast values corresponding to the stock funds to be predicted. Wait for the third training model to modify the stock price prediction value, and perform the step 53 after the third training model is established by the server-side processing module 13, and the server-side processing module 13 performs each modification according to the further test subset Based on the training data, the stock prices of the training stocks, the training funds, and the stock prices of the training corrections are used to determine whether the prediction accuracy of the third training model is greater than a third threshold. When the server-side processing module 13 When it is judged that the prediction accuracy of the third training model is not greater than the third threshold, the server processing module 13 performs step 54, that is, adjusts the third training model and returns to step 53; on the contrary, Specifically, when the server-side processing module 13 determines that the prediction accuracy of the third training model is greater than the third threshold, the server-side processing module 13 performs the step 55, that is, confirms that the third training model is The modified analysis model.

The prediction procedure includes a step 61, a step 62, a step 63, a step 64, a step 65, a step 66, a step 67, and a step 68, which are used to generate a stock price prediction value based on the overall stock market trend.

1 and 5, in step 61, the management terminal processing module 23 generates an analysis request related to the stock based on the input signal generated by the management terminal input module 22 through the input operation of the manager. And it is transmitted to the server 1 via the communication network 100 through the management communication module 21.

Then in step 62, when the server-side processing module 13 receives an analysis request from the management terminal 2 related to the stock through the server-side communication module 11, the server-side processing module 13 according to the related The stock prices of the individual stocks use the technical analysis model, such as the Technical Analysis Library (TA-Lib), to generate multiple technical indicators related to the individual stocks. In detail, the technical indicators refer to A stock’s multiple historical stock prices are calculated based on other data related to the stock, such as candlestick chart, Relative Strength Index (RSI), or Moving Average Convergence (Moving Average Convergence). / Divergence, MACD) and other data, and in the first embodiment, the technical indicators of the individual stocks include the relative strength index corresponding to the individual stock and the exponential moving average of similarity and difference.

Then in step 63, the server-side processing module 13 uses the individual stock analysis model to generate the stock price prediction values corresponding to the individual stocks according to the individual stock technical indicators.

Then in step 64, the server-side processing module 13 uses the technical analysis model to generate multiple fund technical indicators related to the stock fund based on the fund stock prices related to the stock fund. , The Dow Jones Industrial Index Fund includes the 30 largest and most well-known listed companies in the United States, and the constituent stocks of Taiwan 50 include the top 50 listed stocks in Taiwan by market capitalization. In other words, the equity fund contains multiple representative The stocks developed by the stock market can therefore represent the overall stock market trend. On the other hand, the technical indicators of these funds also include the relative strength index corresponding to the stock fund and the moving average of index smoothing.

Then in step 65, the server-side processing module 13 uses the overall analysis model to generate the stock price prediction values corresponding to the stock fund based on the fund technical indicators.

It is worth mentioning that in the first embodiment, the server processing module 13 performs the step 62, the step 63, the step 64, and the step 65 in sequence, but in other embodiments, the The server-side processing module 13 can also perform the step 64 while performing the step 62, which is not limited to this embodiment.

Then, in step 66, the server-side processing module 13 uses the revised analysis model to generate multiple revised stock prices corresponding to the stock based on the stock price forecasts of the stock and the stock fund forecasts. Forecast value. In this way, investors can evaluate whether to invest in the stock based on the revised stock price forecast value, thereby avoiding possible serious asset losses.

Finally, in step 67, the server-side processing module 13 determines whether the revised stock price forecasts are greater than a preset value. When the server-side processing module 13 determines the revised stock price forecasts When the values are greater than the preset value, step 68 is performed immediately, and the server-side processing module 13 generates an analysis result related to the stock. For example, when the server-side processing module 13 determines that the revised stock price forecast values are greater than the preset value, where the preset value is the current stock price of the stock, it means that the stock's future potential will It will rise steadily and produce an analysis result indicating that the stock is a stock with potential and worth investing in. By this, investors who are not familiar with stocks can also choose the stocks they want to invest in based on the analysis result to avoid improper investment Cause asset loss. On the other hand, when the server-side processing module 13 determines that the revised stock price prediction values are not all greater than the preset value, the prediction procedure is ended.

It is supplemented that in the first embodiment, it is executed from the step 61 to the step 68, but in other embodiments, it can also be executed from the step 61 to the end of the step 66 instead of the step. One embodiment is limited.

In summary, the stock prediction method of the present invention mainly uses the server-side processing module 13 to use the technical analysis model based on the stock prices related to the stock and the fund stock prices related to the stock fund. , The stock analysis model, the overall analysis model, and the modified analysis model to obtain the stock price forecast values of the corresponding stocks, the stock price forecast values of the stock funds, and the revised stock prices of the corresponding stocks Forecast value, by this, investors can know whether the stock is a relatively stable stock or not based on the revised stock price forecast value of the fund's stock prices related to the overall stock market trend. Unsound stocks that fluctuate with the overall stock market trend are then invested based on this information to avoid serious asset losses. On the other hand, when the server-side processing module 13 determines that the revised stock price forecasts are all greater than the forecast When setting the value, the server generates an analysis result indicating that the stock is a stock that has potential and is worth investing. In this way, investors who are not familiar with stock investment can also choose the stock to invest based on the analysis result, thereby avoiding Because of the serious loss of assets caused by unfamiliarity with the stock investment market, it is worth mentioning that by adjusting the revised training data, the content included is multiple training stock stock prices, multiple training fund stock prices, and corresponding training stock funds In the current time interval, the multiple training correction fund stock prices, using machine learning algorithms to establish another correction analysis model used to generate multiple correction fund stock price forecasts corresponding to stock funds, so that the present invention can not only be applied to The stock price analysis of individual stocks can also be used to analyze and judge the stock prices of stock funds, so it can indeed achieve the purpose of the invention.

However, the above are only examples of the present invention. When the scope of implementation of the present invention cannot be limited by this, all simple equivalent changes and modifications made in accordance with the scope of the patent application of the present invention and the content of the patent specification still belong to Within the scope covered by the patent of the present invention.

1: Server 100: Communication network 11: Server communication module 12: Server-side storage module 13: Server-side processing module 2: Management side 21: Management terminal communication module 22: Management terminal input module 23: Management end processing module 31~35: Steps 41~45: Steps 51~55: Steps 61~68: Steps

Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: Figure 1 is a block diagram illustrating a stock prediction system implementing a first embodiment of the stock prediction method of the present invention; 2 is a flowchart illustrating a stock analysis model establishment procedure in the first embodiment of the stock prediction method of the present invention; FIG. 3 is a flowchart illustrating an overall analysis model establishment procedure in the first embodiment of the stock prediction method of the present invention; 4 is a flowchart illustrating a modification analysis model establishment procedure in the first embodiment of the stock prediction method of the present invention; and FIG. 5 is a flowchart illustrating a prediction procedure in the first embodiment of the stock prediction method of the present invention.

61~68: Steps

Claims (10)

A stock prediction method implemented by a server connected to a management terminal. The server stores multiple stock prices of individual stocks in a current time interval, and a stock fund related to the overall stock market trend in the current time interval. The multiple fund stock prices in the multiple fund stock prices, one used to generate multiple technical analysis models based on multiple stock prices, and one used to generate multiple technical indicators related to the current time interval based on multiple technical indicators related to a stock to be predicted A stock analysis model for the stock price prediction value of the stock price of a stock to be predicted in a future time interval later than the current time interval, one used to generate multiple technical indicators related to a stock fund to be predicted in the current time interval An overall analysis model related to the stock price forecast value of the stock fund to be predicted in the future time interval, and an overall analysis model for the stock price forecast value of the stock to be forecasted and the stock price forecast value of the stock fund to be forecast The stock price forecast value generates a revised analysis model of multiple revised stock price forecast values in which the stock price forecast values of the stocks to be predicted are affected by the stock price forecast values of the stock funds to be predicted. The stock forecast method includes the following steps: (A) When the server receives an analysis request from the management terminal related to the stock, the server uses the technical analysis model to generate the analysis request related to the stock based on the stock price of the stock. Multiple technical indicators of individual stocks; (B) Through the server, according to the technical indicators of the individual stocks, the individual stock analysis model is used to generate multiple stock price forecasts corresponding to the individual stocks; (C) Using the server to generate multiple fund technical indicators related to the stock fund by using the technical analysis model based on the fund stock prices related to the stock fund; (D) Through the server, based on the fund technical indicators, the overall analysis model is used to generate multiple stock price forecasts corresponding to stock funds; and (E) Using the server, according to the stock price forecasts of the stock and the stock fund forecasts, the revised analysis model is used to generate multiple revised stock price forecasts corresponding to individual stocks. For the stock prediction method described in claim 1, the server also stores a number of individual stock training data corresponding to multiple training stocks, and each stock training data includes the previous time corresponding to the training stock earlier than the current time interval The multiple training stock technical indicators in the interval, and the multiple training stock stock prices in the current time interval, the stock prediction method further includes the following steps: (F) Using the server, the training data of the individual stocks are divided into a training subset and a test subset; (G) Using the server, based on the technical indicators of the training stocks and the stock prices of the training stocks corresponding to the training data of each stock in the training subset, a machine learning algorithm is used to establish a basis related to the to-be-predicted The technical indicators of the individual stock in the current time interval generate the first training models related to the stock price prediction value of the stock to be predicted in a future time interval later than the current time interval; (H) Using the server to determine whether the prediction accuracy of the first training model is greater than the first training model based on the training stock technical indicators and the training stock price corresponding to the training data of each stock in the test subset A threshold (I) When the server determines that the prediction accuracy of the first training model is not greater than the first threshold, the server adjusts the first training model according to the training subset and performs the step (H); and (J) When the server determines that the prediction accuracy of the first training model is greater than the first threshold, the server confirms that the first training model is the stock analysis model. For the stock prediction method described in claim 1, the server also stores multiple fund training data corresponding to multiple training stock funds, and each fund training data includes the corresponding training stock fund earlier than the current time For multiple training fund technical indicators in the previous time interval of the interval, and multiple training fund stock prices in the current time interval, the stock prediction method further includes the following steps: (K) Using the server to divide the fund training data into another training subset and another test subset; (L) Using the server, based on the training fund technical indicators and the training fund stock prices corresponding to the training data of each fund in the other training subset, a machine learning algorithm is used to establish a basis related to the The technical indicators of the stock fund to be predicted in the current time interval generate the second training models related to the stock price prediction value of the stock fund to be predicted in the future time interval; (M) Using the server, according to the training fund technical indicators and the training fund stock prices corresponding to the training data of each fund in the other test subset, determine whether the prediction accuracy of the second training model is greater than A second threshold; (N) When the server determines that the prediction accuracy of the second training model is not greater than the second threshold, the server adjusts the second training model according to the other training subset and performs the step (M );and (O) When the server determines that the prediction accuracy of the second training model is greater than the second threshold, the server confirms that the second training model is the overall analysis model. For the stock prediction method described in claim 1, the server also stores a number of modified training data, and each modified training data includes a number of training stock prices corresponding to the training stock in the current time interval, corresponding to the training stock type The multiple training fund stock prices of the fund in the current time interval, and the multiple training correction individual stock stock prices corresponding to the training stocks in the current time interval, the stock prediction method also includes the following steps: (P) By the server, the modified training data is divided into another training subset and another test subset; (Q) With the server, based on the training stock price, the training fund stock price, and the training correction stock price of each modified training data in the another training subset, a machine learning algorithm is used to create 1. Generate the third training model for the revised stock price forecast values based on the stock price forecast values corresponding to the stocks to be forecasted and the stock price forecast values corresponding to the stock funds to be forecasted; (R) Using the server, based on the training stock price, the training fund stock price, and the training correction stock price of each modified training data in the further test subset, determine the third training model's Whether the prediction accuracy rate is greater than a third threshold; (S) When the server determines that the prediction accuracy of the third training model is not greater than the third threshold, the server adjusts the third training model according to the another training subset and performs the step (R );and (T) When the server determines that the prediction accuracy of the third training model is greater than the third threshold, the server confirms that the third training model is the modified analysis model. The stock prediction method as described in claim 1, after this step (E), further includes the following steps: (U) Use the server to determine whether the revised stock price forecast values are greater than a preset value. When the server determines that the revised stock price forecasts are greater than the preset value, the server generates a Related to the analysis results of the stock. A server terminal used for stock forecasting and connected to a management terminal via a communication network. The server terminal includes: A server-side communication module, connected to the communication network; A server-side storage module that stores multiple stock prices of a single stock in a current time interval, multiple stock prices of a stock fund related to the overall stock market trend in the current time interval, and one for multiple basis A technical analysis model for generating multiple technical indicators for a stock price, one for generating multiple technical indicators related to a stock to be predicted in the current time interval based on multiple technical indicators related to the stock to be predicted one day later than the current time interval The stock analysis model of the stock price prediction value of the stock price in the future time interval, and one is used to generate a plurality of related to the stock fund to be predicted in the future based on the multiple technical indicators related to the stock fund to be predicted in the current time interval The overall analysis model of the stock price forecast value of the stock price in the time interval, and a method used to generate the stock price forecast value of the stock stock to be forecasted and the stock price forecast value of the stock fund to be forecasted. A revised analysis model of multiple revised stock price forecasts whose stock price forecast value is affected by the stock price forecast values of the stock-type fund to be predicted; and A server-side processing module, electrically connected to the server-side communication module and the server-side storage module; Wherein, when the server-side processing module receives an analysis request related to the stock from the management terminal via the communication network through the server-side communication module, the server-side processing module is based on the analysis request related to the stock The stock price of an individual stock is used to generate multiple stock technical indicators related to the stock using the technical analysis model, and the stock analysis model is used to generate multiple stock price forecasts corresponding to the stock based on the technical indicators of the individual stocks. The stock price of these funds of the fund, using the technical analysis model to generate multiple fund technical indicators related to the stock fund, and based on the fund technical indicators, using the overall analysis model to generate multiple stock prices corresponding to the stock fund The predicted value, and based on the predicted value of the stock price of the stock and the predicted value of the stock price of the stock fund, the revised analysis model is used to generate multiple revised stock price forecasts corresponding to the individual stocks. The server according to claim 6, wherein the server storage module further stores a plurality of individual stock training data corresponding to a plurality of training individual stocks, and each stock training data includes the training data corresponding to the training stock earlier than the current time Multiple training individual stock technical indicators in the previous time interval of the interval, and multiple training individual stock prices in the current time interval, the server-side processing module divides the training data of these individual stocks into a training subset and a tester According to the training data of each stock in the training subset and the corresponding training stock technical indicators and the training stock price, a machine learning algorithm is used to establish a basis for the current time interval related to the stock to be predicted The technical indicators generated the first training model related to the stock price prediction value of the stock to be predicted in a future time interval later than the current time interval, and according to the training data corresponding to each stock in the test subset The technical indicators of the training stocks and the stock prices of the training stocks determine whether the prediction accuracy rate of the first training model is greater than a first threshold. When the server-side processing module determines the prediction accuracy rate of the first training model When the value is not greater than the first threshold, the server-side processing module adjusts the first training model and re-determines. When the server-side processing module determines that the prediction accuracy of the first training model is greater than the first threshold Value, the server-side processing module confirms that the first training model is the individual stock analysis model. The server side according to claim 6, wherein the server side storage module also stores a plurality of fund training data corresponding to a plurality of training stock funds, and each fund training data includes the corresponding training stock fund in the morning. For multiple training fund technical indicators in the previous time interval of the current time interval, and multiple training fund stock prices in the current time interval, the server-side processing module divides the fund training data into another training sub Set and another test subset, and based on the training fund technical indicators and the training fund stock prices corresponding to the training data of each fund in the other training subset, a machine learning algorithm is used to establish a basis related to the The technical indicators of the stock fund to be predicted in the current time interval generate the second training models related to the stock price prediction value of the stock fund to be predicted in the future time interval, and according to the other test sub The technical indicators of the training funds and the stock prices of the training funds corresponding to the training data of each fund are collected to determine whether the prediction accuracy of the second training model is greater than a second threshold. When the server-side processing module determines When the prediction accuracy rate of the second training model is not greater than the second threshold, the server-side processing module adjusts the second training model and judges again. When the server-side processing module determines that the second training model is When the prediction accuracy rate is greater than the second threshold value, the server-side processing module confirms that the second training model is the overall analysis model. The server according to claim 6, wherein the server storage module also stores a plurality of modified training data, and each modified training data includes a plurality of training stock prices corresponding to the training stock in the current time interval. The multiple training fund stock prices of the stock fund in the current time interval should be trained, and the multiple training stock price corrections corresponding to the training stocks in the current time interval should be trained. The server-side processing module divides the revised training data into further A training subset and another test subset, and based on the training stock price, the training fund stock price, and the training correction stock price of each modified training data in the another training subset, using a machine learning algorithm Method, establish a third training model that generates the revised stock price forecasts based on the stock price forecasts corresponding to the stocks to be forecasted and the stock fund forecasts corresponding to the stocks to be forecasted, and according to the other test In each of the modified training data in the subset, the stock price of the training stocks, the stock price of the training funds, and the stock price of the training modified stocks are used to determine whether the prediction accuracy of the third training model is greater than a third threshold. When the servo When the end processing module determines that the prediction accuracy of the third training model is not greater than the third threshold value, the server end processing module adjusts the third training model and re-judges. When the server end processing module determines When the prediction accuracy of the third training model is greater than the third threshold, the server-side processing module confirms that the third training model is the modified analysis model. The server side according to claim 6, wherein the server-side processing module also determines whether the revised stock price forecast values are all greater than a preset value, and when the server-side processing module determines the revised stock price forecast values When both are greater than the preset value, the server-side processing module generates an analysis result related to the stock.

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