From Bars to Trades: Unleashing the Potential of LLMs in Tick-Level Alpha Mining

Alpha mining plays a central role in quantitative research by discovering predictive signals from market data. Recent LLM-based approaches have demonstrated the potential of automating alpha mining by generating and refining factor expressions over predefined market features. However, their reliance on predefined features constrains the search space and thereby limits the discovery of predictive patterns that require constructing new variables from fine-grained market events. To address this limitation, we study alpha mining directly from tick-level trade data, formulating it as a hierarchical search over variable construction and factor composition. The enlarged search space makes it difficult to identify which changes improve predictive performance. To this end, we propose CARTs, an LLM-driven framework for tick-level alpha mining. CARTs integrates a tick-level DSL that separates variable construction from factor composition, evidence-guided search with controlled comparisons and research memory for fine-grained credit assignment, and multi-view evaluation beyond predictive performance. Together, these components enable efficient and evidence-driven factor exploration. Experiments show that directly adapting conventional alpha miners to tick data can degrade performance, while CARTs outperforms the strongest tick-level baseline by 88.6% in IC at the same 100-factor library size and generalizes across markets.