{"repo":"CFA-Institute-RPC/The-Automation-Ahead","free":true,"listed":false,"github":"https://github.com/CFA-Institute-RPC/The-Automation-Ahead","clone":"git clone https://github.com/CFA-Institute-RPC/The-Automation-Ahead.git","description":"Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.","language":"Jupyter Notebook","stars":65,"topics":["data-science","deep-learning","finance","financial-data","jupyter-notebook","natural-language-processing","quantitative-finance"],"license":"MIT","category":"trading","readme_excerpt":"The Automation Ahead – Code Repository The Automation Ahead repository is the companion resource for the Automation Ahead Content Series by the CFA Institute Research & Policy Center (RPC). This series explores the transformative role of generative AI (GenAI) in the investment industry, providing actionable insights and practical tools to enhance efficiency and innovation in financial workflows. --- 🔍 How to Use This Repository This repository is designed as a learning tool. We encourage you to: 1. Explore the code examples to understand the specific applications of each GenAI tool. 2. Experiment with modifying and adapting the scripts to fit your own use cases. 3. Join us in pushing the industry forward by contributing your insights and improvements. --- 🌟What’s Inside - Python Code Examples : Each folder includes Python scripts corresponding to the articles, offering hands-on applications of GenAI tools in investment contexts. - Practical Use Cases : Explore applications developed specifically for investment professionals, enabling you to test and assess GenAI’s practicality in real scenarios. --- 📚 About the Series The Automation Ahead Content Series includes: 1. Introduction and Automation Framework: Overview of automation opportunities in the investment industry. 2. Practical Guide for LLMs in the Financial Industry: Hands-on insights into leveraging large language models (LLMs) for financial workflows. 3. Retrieval Augmented Generation (RAG): Deep dive into how RAG w","default_branch":null,"files":null,"tree":[],"storefront":"/r/CFA-Institute-RPC","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/CFA-Institute-RPC/The-Automation-Ahead/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}