# US 2025/0148488 A1 · Stock trend analysis method and apparatus based on machine learning

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- Number: US 2025/0148488 A1
- Document ID: US20250148488A1
- Status: Published application
- Kind: A1
- Application number: 19/012,915
- Filed: 2025-01-08
- Published: 2025-05-08
- Earliest claimed: 2022-08-09
- Assignees: Shenzhen Futu Network Technology Co., Ltd.
- Applicants: Shenzhen Futu Network Technology Co., Ltd.
- Inventors: Jinhui HU; Yuyan RU; Xin XIE
- Web page: https://patentgrep.com/patent/US20250148488A1

## CPC

- G06Q 30/0202: Market predictions or forecasting for commercial activities
- G06Q 40/04: Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
- G06Q 40/06: Asset management; Financial planning or analysis
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

The present application discloses a stock trend analysis method and apparatus based on machine learning, an electronic device, and a storage medium. The method comprises: acquiring a target model and stock data corresponding to a target stock; splitting the target model to obtain a plurality of sub-target models; on the basis of the stock data and the plurality of sub-target models, determining a plurality of trend prediction results corresponding to the target stock; and on the basis of the plurality of trend prediction results, determining a target trend prediction result corresponding to the target stock. According to the present application, the stock data is processed by means of an intelligent model (a target model) to output a trend prediction result corresponding to a target stock, so that the accuracy of the prediction result can be improved.

## Claims

### Claim 1

A stock trend analysis method based on machine learning, applicable to an electronic device, the method comprising:
obtaining a target model and stock data corresponding to a target stock, wherein the stock data is determined based on technical index data and technical morphological data of the target stock, the target model is hot-updated through a cloud object storage (COS) bucket of a public cloud, the COS bucket of the public cloud stores an MD5 value of the target model, and whether the target model is to be hot-updated is determined based on the MD5 value;
dividing the target model to obtain a plurality of sub-target models;
determining a plurality of trend prediction results corresponding to the target stock based on the stock data and the plurality of sub-target models, wherein the plurality of sub-target models are in one-to-one correspondence with the plurality of trend prediction results; and
determining a target trend prediction result corresponding to the target stock based on the plurality of trend prediction results.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the obtaining the target model comprises:
determining whether a target file corresponding to the target model exists locally to obtain a first determination result; and
obtaining the target model based on the first determination result.

### Claim 3 (depends on 2)

The method according to claim 2, wherein the obtaining the target model based on the first determination result comprises:
in response to the first determination result indicating that the target file corresponding to the target model does not exist locally, downloading the target file from an object storage bucket, and decompressing the target file to obtain the target model.

### Claim 4 (depends on 2)

The method according to claim 2, wherein the obtaining the target model based on the first determination result comprises:
in response to the first determination result indicating that the target file corresponding to the target model exists locally, determining whether the target file is a latest version to obtain a second determination result; and
in response to the second determination result indicating that the target file is the latest version, obtaining the target model from the target file.

### Claim 5 (depends on 4)

The method according to claim 4, wherein the obtaining the target model based on the first determination result comprises:
in response to the second determination result indicating that the target file is not the latest version, downloading the target file from an object storage bucket and decompressing the target file to obtain the target model.

### Claim 6 (depends on 1)

The method according to claim 1, wherein the obtaining the stock data corresponding to the target stock comprises:
obtaining the technical index data and the technical morphological data of the target stock;
determining, based on the index data and the morphological data, label data and a derivative index that correspond to the target stock; and
determining the index data, the morphological data, the label data, and the derivative index as the stock data.

### Claim 7 (depends on 1)

The method according to claim 1, wherein the determining the plurality of trend prediction results corresponding to the target stock based on the stock data and the plurality of sub-target models comprises:
inputting, for each of the plurality of sub-target models, the stock data into the sub-target model to obtain a trend prediction result corresponding to the sub-target model; and
aggregating respective trend prediction results corresponding to the plurality of sub-target models to obtain the plurality of trend prediction results.

### Claim 8 (depends on 1)

The method according to claim 1, wherein the determining the target trend prediction result corresponding to the target stock based on the plurality of trend prediction results comprises:
performing statistical analysis on the plurality of trend prediction results to obtain the target trend prediction result, wherein the target trend prediction result comprises a rising-falling trend of the target stock within a predetermined future time period, and a probability corresponding to the rising-falling trend.

### Claim 9 (depends on 1)

The method according to claim 1, further comprising, prior to the obtaining the target model and the stock data corresponding to the target stock:
obtaining an initial model and training data, wherein the initial model is a random forest model; and
training the initial model by using the training data to obtain the target model.

### Claim 10 (depends on 9)

The method according to claim 9, wherein the obtaining the training data comprises:
obtaining K-line data;
calculating initial index data and initial morphological data based on the K-line data;
cleaning the initial index data and the initial morphological data to obtain target index data and target morphological data; and
determining the training data based on the target index data and the target morphological data.

### Claim 11 (depends on 10)

The method according to claim 10, wherein the determining the training data based on the target index data and the target morphological data comprises:
determining label data based on the target index data, the target morphological data, and a first predetermined threshold, wherein the label data characterizes a degree of overbought or oversold indicated by the target index data and the target morphological data;
coupling the label data and historical data to obtain a derivative index, wherein the historical data at least comprises a historical opening price, a historical closing price, and a historical rising or falling amount; and
aggregating the target index data, the target morphological data, the label data, and the derivative index to obtain the training data.

### Claim 12 (depends on 11)

The method according to claim 11, wherein the training the initial model by using the training data to obtain the target model comprises:
inputting the training data into the random forest model and optimizing a hyper parameter in the random forest model through grid search; and
in response to the hyper parameter reaching a second predetermined threshold, obtaining the target model.

### Claim 13

An electronic device, comprising:
a processor;
a memory;
a communication interface; and
one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing a stock trend analysis method based on machine learning, the method comprising:
obtaining a target model and stock data corresponding to a target stock, wherein the stock data is determined based on technical index data and technical morphological data of the target stock, the target model is hot-updated through a cloud object storage (COS) bucket of a public cloud, the COS bucket of the public cloud stores an MD5 value of the target model, and whether the target model is to be hot-updated is determined based on the MD5 value;
dividing the target model to obtain a plurality of sub-target models;
determining a plurality of trend prediction results corresponding to the target stock based on the stock data and the plurality of sub-target models, wherein the plurality of sub-target models are in one-to-one correspondence with the plurality of trend prediction results; and
determining a target trend prediction result corresponding to the target stock based on the plurality of trend prediction results.

### Claim 14 (depends on 13)

The electronic device according to claim 13, wherein the obtaining the target model comprises:
determining whether a target file corresponding to the target model exists locally to obtain a first determination result; and
obtaining the target model based on the first determination result.

### Claim 15 (depends on 14)

The electronic device according to claim 14, wherein the obtaining the target model based on the first determination result comprises:
in response to the first determination result indicating that the target file corresponding to the target model does not exist locally, downloading the target file from an object storage bucket, and decompressing the target file to obtain the target model.

### Claim 16 (depends on 14)

The electronic device according to claim 14, wherein the obtaining the target model based on the first determination result comprises:
in response to the first determination result indicating that the target file corresponding to the target model exists locally, determining whether the target file is a latest version to obtain a second determination result; and
in response to the second determination result indicating that the target file is the latest version, obtaining the target model from the target file.

### Claim 17 (depends on 16)

The electronic device according to claim 16, wherein the obtaining the target model based on the first determination result comprises:
in response to the second determination result indicating that the target file is not the latest version, downloading the target file from an object storage bucket and decompressing the target file to obtain the target model.

### Claim 18 (depends on 13)

The electronic device according to claim 13, wherein the obtaining the stock data corresponding to the target stock comprises:
obtaining the technical index data and the technical morphological data of the target stock;
determining, based on the index data and the morphological data, label data and a derivative index that correspond to the target stock; and
determining the index data, the morphological data, the label data, and the derivative index as the stock data.

### Claim 19 (depends on 13)

The electronic device according to claim 13, wherein the determining the plurality of trend prediction results corresponding to the target stock based on the stock data and the plurality of sub-target models comprises:
inputting, for each of the plurality of sub-target models, the stock data into the sub-target model to obtain a trend prediction result corresponding to the sub-target model; and
aggregating respective trend prediction results corresponding to the plurality of sub-target models to obtain the plurality of trend prediction results.

### Claim 20

A computer-readable storage medium, configured to store a computer program, wherein the computer program, when executed by a processor, implements a stock trend analysis method based on machine learning, the method comprising:
obtaining a target model and stock data corresponding to a target stock, wherein the stock data is determined based on technical index data and technical morphological data of the target stock, the target model is hot-updated through a COS bucket of a public cloud, the COS bucket of the public cloud stores an MD5 value of the target model, and whether the target model is to be hot-updated is determined based on the MD5 value;
dividing the target model to obtain a plurality of sub-target models;
determining a plurality of trend prediction results corresponding to the target stock based on the stock data and the plurality of sub-target models, wherein the plurality of sub-target models are in one-to-one correspondence with the plurality of trend prediction results; and
determining a target trend prediction result corresponding to the target stock based on the plurality of trend prediction results.