AlphaRef: Reference‑Guided Autoregressive Formula Generation for Formulaic Alpha Mining

Discovering predictive formulaic alpha factors from noisy stock-market data is a challenging problem in quantitative finance. Existing symbolic regression methods often search over a vast expression space with limited reuse of established factor structures. Recently, some LLM-based approaches, such as Alpha-GPT, incorporate existing factor knowledge into prompts to guide formula generation. However, effectively using reference formulas to guide generation while preserving the ability to explore beyond them remains a challenge. We propose AlphaRef, a reference-guided autoregressive framework for formulaic alpha mining. AlphaRef converts formulas from the Alpha101 and Alpha158 libraries into reverse Polish notation and progressively masks their suffixes to obtain variable-length prefixes. These prefixes provide generation conditions at different levels of specificity. A Transformer-based generator combines reference prefixes with market data and factor-pool information to autoregressively generate candidate formulas. During training, a reference-decay schedule gradually reduces the use of reference prefixes, enabling a transition from reference-conditioned completion to generation without explicit references. Experiments on the CSI 300, CSI 500, and CSI 1000 stock universes in the Chinese A-share market show that AlphaRef achieves higher mean information coefficients and rank information coefficients than the baselines in the main comparison. Under the evaluated backtesting settings, AlphaRef also achieves the highest Sharpe ratios and compound annual growth rates across all three stock universes. These results support the effectiveness of reference-guided autoregressive generation for formulaic alpha mining.