Stock trend analysis method and apparatus based on machine learning

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.

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.