Enhanced Reinforcement Learning-Based Alpha Generation for Trading with Expanded Search Space

In this paper, researchers propose a new method for generating alpha factors, which are key to quantitative trading. The traditional approach involves human intervention in creating expression formulas for alpha factors, but the proposed method leverages reinforcement learning algorithms to automate the process. The search space defines which operators and operands to use in creating symbolic factors, and a pregenerated seed formulaic alpha set is used to initialize the search.
To understand this concept, imagine building a Lego castle. Traditionally, someone might have built the castle piece by piece, but with reinforcement learning, the algorithm can generate the entire castle design based on the available blocks. The same approach is applied to alpha factor generation, where the algorithm generates formulaic expressions for the factors based on predefined rules and search space parameters.
The proposed method improves upon previous approaches by considering the performance of combined alpha factors rather than just individual ones. This is like building a Lego castle with multiple levels, each level contributing to the overall structure’s stability and beauty. By combining alpha factors, the algorithm can generate a more robust and efficient trading strategy.
Another challenge in alpha factor generation is the need for explainability, as black-box models can be difficult to interpret. The proposed method addresses this issue by providing a more transparent and understandable approach to generating alpha factors.
In summary, the article proposes a reinforcement learning-based method for generating alpha factors that improves upon traditional approaches by considering combined alpha factors and addressing the need for explainability. By leveraging machine learning algorithms, the proposed method can generate formulaic expressions for alpha factors more efficiently and effectively than traditional methods.