For a project I am working on, I am aiming to predict market trends and make long or short plays as a result. I am looking to use a reinforcement algorithm for this. In a paper I read recently however, the authors suggested using a two tiered system; an SVM classifier to determine market trend and three algorithms based on positive, negative or sideways market trend. Therefore, each algorithm is trained with data of the same trend so there exists less variability.
My question is, would using three algorithms improve the accuracy of the result, or would one model (with the same amount of data in total) provide the same accuracy?
Apologies if this seems a very basic question, I am new to machine learning and am eager to learn. Cheers
Different models have different strengths and weaknesses. This is the entire idea behind using an ensemble model.
What you can do is train a random forest or adaboost