Large Language Models (LLMs) have demonstrated notable capabilities across financial tasks, including financial report summarization, earnings call transcript analysis, and asset classification. However, their real-world effectiveness in managing complex fund investment remains inadequately assessed. A fundamental limitation of existing benchmarks for evaluating LLM-driven trading strategies is their reliance on historical back-testing, inadvertently enabling LLMs to "time travel"—leveraging future information embedded in their training corpora, thus resulting in possible information leakage and overly optimistic performance estimates. To address this issue, we introduce DeepFund, a live fund benchmark tool designed to rigorously evaluate LLM in real-time market conditions. Utilizing a multi-agent architecture, DeepFund connects directly with real-time stock market data—specifically data published after each model’s pretraining cutoff—to ensure fair and leakage-free evaluations. Empirical tests on nine flagship LLMs from leading global institutions across multiple investment dimensions—including ticker-level analysis, investment decision-making, portfolio management, and risk control—reveal significant practical challenges. Notably, even cutting-edge models such as DeepSeek-V3 and Claude-3.7-Sonnet incur net trading losses within DeepFund real-time evaluation environment, underscoring the present limitations of LLMs for active fund management. Our code is available at https://github.com/HKUSTDial/DeepFund.

Data transformation poses significant challenges due to the wide diversity in input data formats and different requirements. Existing approaches—including human-driven, algorithmic, and large language model (LLM)-based solutions—each exhibits trade-offs in terms of cost, accuracy, and the range of supported transformations. To address these limitations, we propose MegaTran , a novel framework for generating accurate and cost-effective data transformation code. MegaTran employs a two-stage process: Weak2StrongPrompt , which converts a user's weak prompt (a loosely specified user input) into a strong, structured prompt, and Prompt2Code , which generates transformation code based on this refined prompt. In Weak2StrongPrompt , a fine-tuned lightweight LLM predicts the transformation type and generates a detailed task description from the user's input. In Prompt2Code , a powerful LLM generates the corresponding transformation code, guided by two key optimizations: (1) Sanity-check Reflection with checklist , which iteratively debugs and refines the code by addressing errors; and (2) Lazy-RAG , a retrieval-augmented generation technique that retrieves relevant code snippets or documentation from external resources ( e.g. , GitHub, DataPrep) to enhance code quality. Extensive experiments show that MegaTran achieves results varying from +2.2% to +26.1% accuracy improvement compared with SoTA methods.