AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning
We introduce AlphaPilot, a reinforcement-learning framework that trains multi-turn tool-using LLM agents for adaptive alpha mining.
ProofPaper ↗
Key points
- Formulaic alpha mining seeks interpretable expressions that rank assets by future returns.
- The task is challenging because it demands simultaneous processing of unstructured market descriptions, historical data, and quantitative evaluation of candidate factors, yet most existing methods exploit only a subset of these signals.
- The agent conditions on market descriptions, proposes factor expressions, queries real-data evaluator feedback, and updates a retained factor pool across turns.
- At inference time, AlphaPilot performs day-by-day search and pool updates to adapt to shifting market regimes.
Sources (1)
- [1]AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 09:43 AM
We introduce AlphaPilot, a reinforcement-learning framework that trains multi-turn tool-using LLM agents for adaptive alpha mining.
Formulaic alpha mining seeks interpretable expressions that rank assets by future returns.
Extractive summary: sentences quoted from the sources.
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