Ultimate FINGPT for Financial Analysis Build, Train, and Deploy FINGPT Models to.epub
大小:(17.98 MB)

只需: RMB 19元  马上下载

2025最新资料。
660多页内容丰富的资料！
全部矢量文字，方便翻译学习！更适合投喂给大模型，形成自己的知识库！
Ultimate FINGPT for Financial Analysis: Build, Train, and Deploy FINGPT Models to Automate Financial Reporting, Forecast Market Trends, Analyze Sentiment, and Drive Data-Driven Decision-Making FinGPT 金融分析实战：从零构建、训练与部署模型 —— 自动化财报生成、市场趋势预测、情感分析及数据驱动决策

This  resource explores many essential aspects of using generative AI in the field of financial analysis, with a focus on FINGPT—a specialized language model designed for finance. It introduces the growing importance of AI-powered automation in finance, particularly in transforming raw data into meaningful insights for decision-making. The  resource highlights the significance of real-time data interpretation in dynamic financial markets, and showcases how FINGPT can be used to enhance prediction, reporting, and investment strategies. FINGPT stands at the intersection of finance and technology. With its ability to understand financial language and simulate analyst-like reasoning, it becomes a vital tool for professionals in the finance industry. This  resource provides readers with an applied understanding of how to use FINGPT to automate tasks, such as financial reporting, sentiment analysis, risk scenario generation, and market trend prediction. The  resource takes a practical approach to help learners, professionals, and data scientists implement FINGPT in real-world finance workflows. It includes hands-on examples, simulated case studies, and guided exercises that demonstrate the end-to-end usage of FINGPT—from data pre-processing and model training to financial insight generation. Whether you work in banking, asset management, fintech, or analytics, this  resource provides clear strategies for integrating generative AI into your daily financial operations. This  resource is divided into 10 chapters, each covering a focused area of FINGPT implementation in finance. The structure is designed to help learners gradually build expertise, with both the technology, and its financial applications. Chapter 1 introduces the concept of FINGPT, its architecture, and why Generative AI is relevant to the finance industry. It also explains how FINGPT is different from generic Large Language Models, and how it can simulate financial intelligence. Chapter 2 provides a step-by-step guide for setting up the FINGPT development environment. It includes installing the required libraries, configuring cloud note resources (such as, Google Colab), and accessing financial data from APIs such as Yahoo Finance and FRED. Chapter 3 discusses how to clean and prepare financial datasets—both structured and unstructured. It covers tasks, such as handling missing values, normalizing financial time-series data, and preparing textual data (such as earnings call transcripts) for analysis. Chapter 4 focuses on fine-tuning the FINGPT model, using domain-specific financial datasets. It explains training workflows, hyperparameter tuning, and evaluation, using real-world performance metrics. Chapter 5 includes detailed case studies demonstrating how FINGPT can be applied to financial tasks, such as credit risk assessment, portfolio forecasting, and sentiment-aware investment decision-making. Chapter 6 shows how FINGPT can be used to automate financial report generation, turning raw data into structured summaries such as quarterly reports or company overviews. Chapter 7 demonstrates how to use FINGPT for market trend prediction. It shows how to identify early signals, interpret AI-generated insights, and reallocate investments, before broader market recognition. Chapter 8 explains the use of FINGPT for sentiment analysis in finance, including the extraction of named entities, sentiment scoring, and its application in trading signals and asset allocation. Chapter 9 covers performance optimization and scaling of FINGPT, including distributed training, managing large datasets, and applying the model across departments or institutions. Chapter 10 concludes the  resource by highlighting future directions, challenges, and innovations in financial AI, including real-time AI agents, explainability, and ethical considerations in automated finance. This  resource serves as a comprehensive guide for any finance professional or AI enthusiast looking to integrate FinGPT into real-time analytical workflows. By the end of this  resource, readers will be well-equipped to harness the full potential of generative AI in the world of financial analysis and decision-making.