Paper: arXiv 2609.39420

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a highly rigorous empirical study on specializing LLMs for algorithmic trading code generation, introducing a new benchmark and detailed evaluation. While the mathematical complexity is moderate, the empirical setup and results are very strong, demonstrating significant novelty in its approach to domain-specific code generation for finance.

Research Flowchart

  flowchart TD
    A[Research Goal: Specialize LLMs for Executable Algorithmic Trading Code] --> B{Methodology: Continued Pretraining & SFT};
    B -- Data/Inputs --> C[Algorithmic Trading Framework Code; Agent-Validated Request-to-Code Pairs];
    C --> D[Computational Processes: Model Training, Evaluation on QuantCode-Bench];
    D --> E[Outcomes: Improved Judge Pass Rate & Backtest Success];
    E --> F[Key Findings: CP & SFT Address Distinct Failure Modes; Capability Retention Issues Identified];