QuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code
- 类型：arxiv
- 标识：2609.39420
- 链接：https://arxiv.org/abs/2609.39420
- 主分类：engineering
- 形态：method
- TLDR：Large language models are strong general-purpose code generators, but executable algorithmic trading remains a demanding specialization target: a model must translate a natural-language strategy specification into correct program logic for a specialized trading framework, execute on historical data, produce trades, and remain semantically faithful to the request. We study two complementary mechanisms for specializing language models for this setting: continued pretraining on algorithmic-trading framework code and supervised fine-tuning (SFT) on agent-validated request-to-code pairs. Evaluation
- 待LLM分类：否
- 来源文件：
- /inbox/tom/_candidates/2026-10-06-agent-rag-longcontext-candidates.json