QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies

1. Problem Statement

While Large Language Models (LLMs) have demonstrated proficiency in general programming tasks, their ability to generate executable algorithmic trading strategies remains underexplored. Existing benchmarks (e.g., SWE-Bench, LiveCodeBench) focus on general code repair or repository-level engineering but fail to capture the unique constraints of financial domain-specific code generation.

Generating a trading strategy requires a model to:

1. Interpret natural language descriptions of trading ideas (indicators, entry/exit conditions, position management).
2. Translate these ideas into syntactically correct code for a specific framework (Backtrader).
3. Execute successfully on historical data without runtime errors.
4. Produce actual trades (strategies that compile but never trade are functionally useless).
5. Align semantically with the original prompt (avoiding generic templates that happen to work but do not solve the specific task).

The core challenge is that syntactic correctness is insufficient; the code must be logically sound, executable, and semantically faithful to the financial intent.

2. Methodology

2.1 The Benchmark: QuantCode-Bench

The authors introduce QuantCode-Bench, a dataset of 400 tasks designed to evaluate LLMs on the Backtrader framework.

• Data Sources: Tasks were collected from Reddit (183), TradingView (100), StackExchange (90), GitHub (19), and synthetic sources (8).
• Difficulty: Tasks are categorized as Easy, Medium, or Hard based on complexity and detail.
• Language: All prompts are in English.

2.2 Evaluation Pipeline

The benchmark employs a strict, four-stage nested validation pipeline. A strategy is only considered a "Success" if it passes all stages sequentially:

1. Compilation: The code must be syntactically correct and parse without errors.
2. Backtest Execution: The strategy must run successfully on provided historical market data across diverse assets/timeframes without runtime crashes.
3. Trade Generation: The strategy must place at least one trade. (This filters out "dummy" strategies that compile but have logic preventing execution).
4. LLM Judge: An LLM acts as a judge to verify semantic alignment. It checks if the implemented logic (indicators, conditions, rules) matches the original textual description, ensuring the model didn't just generate a working but irrelevant template.

2.3 Evaluation Settings

The paper evaluates models in two distinct modes:

• Single-Turn (One-Shot): The model must generate a correct strategy on the first attempt. This measures initial domain knowledge and zero-shot capability.
• Agentic Multi-Turn: The model receives structured feedback (error type and system message) after each failure and can iterate up to 10 times to repair the code. This measures the model's ability to debug and refine logic.

3. Key Contributions

1. Specialized Benchmark: The first benchmark specifically targeting the generation of executable algorithmic trading strategies, moving beyond simple code completion.
2. Multi-Level Evaluation Framework: A novel pipeline that distinguishes between technical executability, behavioral validity (trades), and semantic correctness (LLM Judge).
3. Comprehensive Model Comparison: A broad evaluation of state-of-the-art models (including Claude, GPT, Gemini, Kimi, DeepSeek, and Qwen families) in both single-turn and agentic settings.
4. Error Analysis: A detailed taxonomy of failure modes, identifying that the primary bottlenecks are not syntax but logic operationalization and API usage.
5. Open Release: The dataset, code, and leaderboard are publicly available to foster research in financial domain-specific code generation.

4. Key Results

4.1 Single-Turn Performance

• Syntax is Solved: Frontier models achieve nearly 100% compilation rates.
• Execution Gap: Performance drops significantly at later stages. While top models reach 98% backtest success, the Judge Pass rate (final success) for the best models (e.g., Claude Opus 4.6) is only **76%**.
• Main Bottleneck: The gap between "Backtest Pass" and "Judge Pass" indicates that models often generate code that runs but fails to implement the specific trading logic requested (e.g., wrong indicators or conditions).

4.2 Agentic Multi-Turn Performance

• Significant Improvement: With iterative feedback, the best models achieve a Judge Pass rate of 95–98%.
• Repairability: This suggests that a large portion of errors in the single-turn setting are "locally repairable" (e.g., fixing a specific API call or condition) rather than fundamental misunderstandings of the task.
• Persistent Issues: Even in the agentic setting, some failures remain due to deep semantic misinterpretations of the natural language prompt.

4.3 Failure Mode Analysis

• Dominant Errors: The most common failure is "Signal conditions do not activate on data" (17.8% in single-turn). This occurs when logic is too strict or indicators are misconfigured, resulting in zero trades.
• API Handling: A significant number of errors involve incorrect handling of Backtrader's Line objects (e.g., using boolean contexts without indexing [0]).
• Semantic Drift: In the agentic setting, "Strategy doesn't match task" becomes a more prominent cause of final failure, indicating that iterative debugging fixes code errors but struggles to correct fundamental misinterpretations of the user's intent.

5. Significance and Implications

• Shift in Difficulty: The paper concludes that for modern LLMs, syntax is no longer the bottleneck. The challenge has shifted to operational formalization—translating abstract financial concepts into executable, data-driven logic.
• Importance of Agentic Workflows: The results strongly support the use of agentic, multi-turn workflows for complex domain tasks. A model's ability to self-correct is as critical as its initial generation capability.
• Limitations of General vs. Specialized Models: Surprisingly, general-purpose models with strong instruction-following capabilities often outperform models specialized solely in coding. This suggests that understanding the financial intent is more critical than raw coding speed.
• Necessity of Semantic Validation: Technical metrics (compilation, execution) are insufficient for open-ended tasks. The inclusion of an LLM Judge is essential to prevent models from "gaming" the system by generating technically correct but semantically irrelevant solutions.
• Future Directions: The benchmark highlights the need for evaluation frameworks that go beyond code generation to include domain-specific behavioral validation, serving as a foundation for future research in financial AI agents.