Can LLMs Design Strategies for Investor Goals? Goal-Oriented Quantitative Strategy Generation

Developing quantitative strategies requires searching over factors, trading rules, and parameters through iterative backtesting and refinement to meet investment objectives. LLMs could assist this process using financial knowledge and backtesting feedback, but their ability to find strategies satisfying specified goals remains insufficiently evaluated. To address this gap, we introduce AlphaGoal, a benchmark for goal-directed strategy search with limited backtest evaluations. It provides 175 validated atomic strategies and 2,221 strategy-construction tasks, each specifying an investment goal in natural language. Grounded in up to 11.5 years of daily data across 5,941 stocks, these tasks cover strategy generation and parameter optimization. Each goal is empirically reachable, with success verified programmatically under a unified historical backtesting protocol. We also introduce AlphaLoop, a reference baseline combining factor information and backtesting feedback to construct and refine strategies. Across six LLMs, AlphaLoop improves overall pass rates over Vanilla, while hard strategy-generation tasks remain challenging.