Developing and Maintaining Machine Learning Models for Predicting Stock Returns at Scale

AI & Machine learning
1

I've successfully developed a machine learning model to predict the next expected return for a single stock, classifying returns into positive, negative, and no return categories. The model performs well, but I now need to scale this approach to handle multiple stocks. The characteristics of each stock may vary, necessitating the creation of individual models.
Can anyone guide me on how to efficiently create and maintain a large number of stock-specific machine learning models? I'm looking for insights on the architecture, the number of files, connections, and workflow to follow. Are there established practices or applications that aid in the development and maintenance of numerous machine learning models for stocks? Any advice on handling diverse stock characteristics and ensuring scalability would be highly appreciated.
2

Hi,



If you want to manage multiple stock-specific machine learning models, having a modular and parameterized code architecture is important, ensuring flexibility for individual stock characteristics. Basically, you can create a standardized data preprocessing pipeline with room for customization and develop both common and stock-specific features. The next step can be implementing a systematic approach to model training and tuning, considering parallelization for efficiency. You should also leverage version control, containerization, and cloud services for consistency, scalability, and resource optimization. Lastly, workflow automation, logging, and comprehensive documentation are equally essential for streamlined processes.



Hope this helps!

3

Is there any best way to start learning the things you mentioned above and is everything can be done using the mlops application, please suggest me how to start learning it.

4

Hi,



For version control, Pro Git book is a great resource, and platforms like GitHub provide interactive tutorials. Docker's official documentation and online tutorials can be referred to for containerization. You can also go through books like Building Machine Learning Powered Applications for MLOps, Apache Airflow/GitHub Actions for workflow automation. Also, for cloud services, the official documentation should be good for starters. For the MLOps application, you can explore MLflow, Kubeflow, etc. 



Hope this helps!



Thanks,

Akshay