Language models specialized to generate executable trading strategies
QuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code
Computation and LanguageMachine Learning
Summary
Writing code for algorithmic trading programs is hard because the code must do exactly what a trader wants, run correctly on data, and generate trades. The authors studied how to fine-tune large language models so they get better at creating such specialized trading code. They trained these models more on trading framework code and then improved them further with feedback from test runs. They measured success by how many strategies passed tests and how well models fixed problems after feedback. Their approach improved the models’ ability to write correct and executable trading code.
What this means in practice
- •For quantitative trading teams: Generate executable algorithmic trading strategies directly from natural language instructions within specific trading frameworks.$Commercial implications: This enables firms to automate strategy development and reduce manual coding, creating marketable AI-assisted trading tools.
- •For software engineers building finance tools: Improve domain-specific code generation for financial frameworks by applying specialized pretraining and fine-tuning techniques.
Authors
Alexey Chernysh, Orkhan Ekhtibarov, Dmitry Zmitrovich
Abstract
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 is centered on QuantCode-Bench, our 400-task benchmark for Backtrader strategy generation, together with a repository-level SWE-bench-like track. Continued pretraining improves single-turn Judge Pass from 41.5% to 47.5% for Qwen3.5-397B-A17B and from 27.8% to 33.0% for Qwen3.6-35B-A3B. SFT applied after continued pretraining yields a larger gain for Qwen3.6-35B-A3B, reaching 58.2% Judge Pass and 83.5% successful backtests; in agentic evaluation it raises first-turn success from 22.3% to 58.3% and final success after up to 10 turns from 47.5% to 79.5%. Continued pretraining alone improves first-turn agentic success but lowers final success after repair from 47.5% to 32.5%, consistent with degraded instruction following, whereas SFT improves both. We also identify a capability-retention failure: domain specialization degrades parser-conformant structured tool calling, and targeted recovery SFT restores tool-call formatting but not the base checkpoint's repository-level agent performance. The results show that framework-oriented pretraining, validated SFT, and explicit capability-retention evaluation address distinct failure modes in domain-specific executable code generation.