AlphaForgeBench: An End-to-End Benchmark Framework for Designing Trading Strategies Using Large Language Models

AlphaForgeBench is an end-to-end benchmark framework focused on evaluating the ability of large language models (LLMs) to design financial trading strategies, covering the complete process from strategy conception to backtesting validation.

Original Author/Maintainer: mmmingxuan Source Platform: GitHub Original Link: https://github.com/mmmingxuan/AlphaForgeBench Release Date: May 27, 2026

This framework aims to systematically measure the performance of LLMs throughout the entire trading strategy development process, providing an evaluation tool for the integration of AI and quantitative finance.

As a core area of fintech, traditional quantitative trading development processes are complex and have high barriers, requiring deep financial knowledge, programming skills, and market experience. With the rise of LLMs, their strong reasoning and code generation capabilities have raised the question: Can they independently design effective trading strategies?

AlphaForgeBench was created to answer this question, focusing on evaluating the performance of LLMs throughout the entire trading strategy design process.

AlphaForgeBench is an open-source end-to-end evaluation framework whose core goal is to measure the capability boundaries of LLMs in financial trading strategy development, simulating the work mode of real quantitative researchers (from data understanding to backtesting validation).

Core evaluation dimensions include:

1. Strategy Conception Ability: Proposing reasonable ideas based on market environment, asset classes, etc.
2. Code Generation Quality: Correct syntax, clear logic, and executability (handling time series, technical indicators, etc.)
3. Backtesting Performance: Risk-adjusted return metrics such as return rate, Sharpe ratio, maximum drawdown, etc.
4. Robustness and Adaptability: Parameter sensitivity and stability under different market environments.

The framework adopts a modular design, with core components including:

• Documentation and Presentation Layer: GitHub Pages provides the project homepage, framework diagrams, and documentation
• Evaluation Engine: Executes strategy backtesting, calculates performance metrics, and generates evaluation reports
• Dataset Interface: Connects to financial market data sources
• Benchmark Model Comparison: Supports parallel testing and comparative analysis of multiple LLMs

The modular design facilitates the integration of new models, addition of tasks, or expansion of data sources.

The importance of this benchmark framework is reflected in:

1. Lowering the Threshold for Quantitative Research: Enabling individual investors and small institutions to access research capabilities similar to those of large hedge funds
2. Accelerating Strategy Iteration: AI assistance shortens the cycle from idea to validation, allowing researchers to focus on optimization and risk management
3. Pushing the Boundaries of LLM Capabilities: Financial strategy design requires a combination of logical reasoning, mathematical computation, code generation, and domain knowledge, serving as a rigorous test of LLMs' comprehensive capabilities and helping to improve models.

Potential Application Scenarios:

• Model Developers: Evaluate the performance of new LLMs in financial tasks
• Quantitative Researchers: Quickly validate strategy ideas or generate code templates
• Educational Institutions: Teaching and research in the intersection of fintech and machine learning
• Investment Institutions: Assess the reliability of AI-generated strategies as a reference for decision-making

Outlook: With the improvement of LLM capabilities and advances in financial data technology, the framework will become an important bridge connecting AI research and financial practice.

AlphaForgeBench represents a cutting-edge exploration of the integration of AI and quantitative finance. It is not only a technical tool but also a preview of the future form of "AI quantitative researchers". Whether LLMs can fully replace human analysts or not, the end-to-end evaluation method provides a valuable perspective for understanding the real capabilities of AI in the financial field.

It is recommended that researchers and practitioners focusing on AI applications, quantitative investment, or fintech continue to pay attention to this framework.