This paper introduces AlphaFin, a benchmarking framework for financial analysis using retrieval-augmented stock-chain methods. The task of financial analysis includes stock trend prediction and financial question answering. Traditional machine learning and deep learning (ML&DL) methods are effective for stock trend prediction but lack interpretability and cannot integrate textual information. Large language models (LLMs) excel in text understanding but suffer from hallucinations and lack real-time data integration. To address these challenges, the authors release AlphaFin, a dataset combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. They then benchmark a state-of-the-art method called Stock-Chain, which integrates retrieval-augmented generation (RAG) techniques. The Stock-Chain framework is designed to predict stock trends and provide financial Q&A by integrating real-time market data and macroeconomic news through RAG. Experimental results show that Stock-Chain achieves state-of-the-art accuracy and over 30% annualized rate of return (ARR). It also provides comprehensive analysis in financial Q&A, enhancing investor confidence and supporting investment decisions. The framework is evaluated using extensive experiments, including ablation studies, GPT4&human preference evaluations, and case studies. The contributions include formalizing the financial analysis task, proposing AlphaFin datasets, fine-tuning StockGPT based on AlphaFin, and integrating RAG to address LLM hallucinations and real-time data limitations. The Stock-Chain framework outperforms baseline methods in financial analysis tasks, demonstrating its effectiveness in stock trend prediction and financial Q&A. The paper also discusses related work, including financial datasets and algorithms, and highlights the importance of RAG in improving LLM performance in financial domains. Overall, the study provides a comprehensive solution for financial analysis using advanced LLM techniques.This paper introduces AlphaFin, a benchmarking framework for financial analysis using retrieval-augmented stock-chain methods. The task of financial analysis includes stock trend prediction and financial question answering. Traditional machine learning and deep learning (ML&DL) methods are effective for stock trend prediction but lack interpretability and cannot integrate textual information. Large language models (LLMs) excel in text understanding but suffer from hallucinations and lack real-time data integration. To address these challenges, the authors release AlphaFin, a dataset combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. They then benchmark a state-of-the-art method called Stock-Chain, which integrates retrieval-augmented generation (RAG) techniques. The Stock-Chain framework is designed to predict stock trends and provide financial Q&A by integrating real-time market data and macroeconomic news through RAG. Experimental results show that Stock-Chain achieves state-of-the-art accuracy and over 30% annualized rate of return (ARR). It also provides comprehensive analysis in financial Q&A, enhancing investor confidence and supporting investment decisions. The framework is evaluated using extensive experiments, including ablation studies, GPT4&human preference evaluations, and case studies. The contributions include formalizing the financial analysis task, proposing AlphaFin datasets, fine-tuning StockGPT based on AlphaFin, and integrating RAG to address LLM hallucinations and real-time data limitations. The Stock-Chain framework outperforms baseline methods in financial analysis tasks, demonstrating its effectiveness in stock trend prediction and financial Q&A. The paper also discusses related work, including financial datasets and algorithms, and highlights the importance of RAG in improving LLM performance in financial domains. Overall, the study provides a comprehensive solution for financial analysis using advanced LLM techniques.