3 papers in the archive (2025), average empirical rigor 7.7/10, average math complexity 5.3/10, 1 with linked code. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in Reinforcement Learning for Trading, High-Frequency Trading & Optimal Execution, NLP & LLMs in Finance, Market Microstructure.
Frequent co-authors: Hongjun Ding (2), Binqi Chen (2), Jinsheng Huang (2), Taian Guo (2), Luchen Liu (2), Zhengyang Mao (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
The automated mining of predictive signals, or alphas, is a central challenge in quantitative finance. While Reinforcement Learning (RL) has emerged as a promising paradigm for generating formulaic alphas, existing frameworks are fundamentally hampered by a triad of interconnected issues. First, the
Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery,
In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to