Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning
This work addresses the challenge of efficient alpha factor mining in stock markets for quantitative traders, representing an incremental enhancement to existing reinforcement learning-based methods.
The paper tackled the problem of discovering formulaic alpha factors for quantitative trading by expanding the search space and using pretrained seeds to generate synergistic alphas, resulting in significant performance improvement in real investment simulations on CSI300 market data.
Mining of formulaic alpha factors refers to the process of discovering and developing specific factors or indicators (referred to as alpha factors) for quantitative trading in stock market. To efficiently discover alpha factors in vast search space, reinforcement learning (RL) is commonly employed. This paper proposes a method to enhance existing alpha factor mining approaches by expanding a search space and utilizing pretrained formulaic alpha set as initial seed values to generate synergistic formulaic alpha. We employ information coefficient (IC) and rank information coefficient (Rank IC) as performance evaluation metrics for the model. Using CSI300 market data, we conducted real investment simulations and observed significant performance improvement compared to existing techniques.