AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery
To address this, we introduce AlphaRLM, a reinforcement-learning framework that trains LLMs to construct executable symbolic factors from financial literature without input factor expressions.
ProofPaper ↗
Key points
- Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space.
- Recent LLM-based approaches use prompting, iterative search, or seed-factor evolution, leaving direct factor construction through policy learning less explored.
- AlphaRLM incorporates Literature-Guided Operator Assembly (LEGO), which conditions factor construction on literature-derived ideas and executable operators, providing semantic priors for exploration.
- Extensive experiments on CSI 300, CSI 1000, and S&P 500 show that AlphaRLM outperforms strong factor-mining and LLM-based baselines in annualized excess return.
Sources (1)
- [1]AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor DiscoveryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 02:32 PM
To address this, we introduce AlphaRLM, a reinforcement-learning framework that trains LLMs to construct executable symbolic factors from financial literature without input factor expressions.
Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space.
Extractive summary: sentences quoted from the sources.
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