Book Image

The Future of Finance with ChatGPT and Power BI

By : James Bryant, Aloke Mukherjee
2.5 (4)
Book Image

The Future of Finance with ChatGPT and Power BI

2.5 (4)
By: James Bryant, Aloke Mukherjee

Overview of this book

In today's rapidly evolving economic landscape, the combination of finance, analytics, and artificial intelligence (AI) heralds a new era of decision-making. Finance and data analytics along with AI can no longer be seen as separate disciplines and professionals have to be comfortable in both in order to be successful. This book combines finance concepts, visualizations through Power BI and the application of AI and ChatGPT to provide a more holistic perspective. After a brief introduction to finance and Power BI, you will begin with Tesla's data-driven financial tactics before moving to John Deere's AgTech strides, all through the lens of AI. Salesforce's adaptation to the AI revolution offers profound insights, while Moderna's navigation through the biotech frontier during the pandemic showcases the agility of AI-focused companies. Learn from Silicon Valley Bank's demise, and prepare for CrowdStrike's defensive maneuvers against cyber threats. With each chapter, you'll gain mastery over new investing ideas, Power BI tools, and integrate ChatGPT into your workflows. This book is an indispensable ally for anyone looking to thrive in the financial sector. By the end of this book, you'll be able to transform your approach to investing and trading by blending AI-driven analysis, data visualization, and real-world applications.
Table of Contents (13 chapters)
Free Chapter
1
Part 1: From Financial Fundamentals to Frontier Tech: Navigating the New Paradigms of Data, EVs, and AgTech
6
Part 2: Pioneers and Protectors: AI Transformations in Software, Finance, Biotech, and Cybersecurity

Visual alchemy: transmuting raw data into golden insights with Power BI

Step into the Python-powered realm of conservative trading, where every line of code is a stepping stone toward financial prudence and optimized gains. Our section unfolds by introducing you to the Python libraries crucial for data manipulation and market data extraction: pandas and yfinance. The script begins by declaring variables such as stock symbols, number of shares, and target prices, effectively laying down the groundwork for your conservative trading strategy. With a mere snippet of code, we transform these raw variables into a structured data frame called stock_df, which is then saved as a CSV file for easy accessibility. Our get_stock_price function keeps your strategies tethered to market realities by pulling in real-time stock prices from Yahoo Finance. This data nourishes another DataFrame, positions_df, which serves as your real-time ledger for tracking share values. We also reserve a spot for tracking...